First public, anonymized version

README.md

@@ -1,9 +1,10 @@
 ---
-title: Lorentz Fillmore
-emoji: 🦀
-colorFrom: indigo
+title: Lorentz Wyl Plots
+emoji: 🏢
+colorFrom: green
 colorTo: yellow
 sdk: streamlit
+python_version: 3.9
 sdk_version: 1.33.0
 app_file: app.py
 pinned: false

_perception_cache.json

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"focus-assassin": 0.0009708268917165697, "focus-victim": 0.6603499054908752, "focus-object": -0.8346922397613525, "focus-concept": -0.8646243810653687}, "37-year-old Chinese woman murdered in her apartment in Rome": {"blame-assassin": 0.6055805087089539, "blame-victim": 0.06709443032741547, "blame-object": -0.5177412033081055, "blame-concept": -1.0713242292404175, "blame-none": -0.6910431385040283, "cause-human": 0.7305833697319031, "cause-object": -0.5928172469139099, "cause-concept": -0.6728085875511169, "cause-none": -0.09410987049341202, "focus-assassin": 0.5365990400314331, "focus-victim": 0.6314513683319092, "focus-object": -0.8306517601013184, "focus-concept": -1.317374348640442}, " Rome, Quadraro neighborhood: the man suspected to have killed by stabbyng his wife has been arrested": {"blame-assassin": 1.1464053392410278, "blame-victim": -0.8487230539321899, "blame-object": -0.5031872987747192, "blame-concept": -0.9854881763458252, "blame-none": -1.0015164613723755, 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"blame-victim": -0.19103139638900757, "blame-object": -0.67251056432724, "blame-concept": 0.8284747004508972, "blame-none": -0.9276074171066284, "cause-human": 0.7143720388412476, "cause-object": -0.6459435224533081, "cause-concept": 0.11336245387792587, "cause-none": -0.7097887992858887, "focus-assassin": 1.6332504749298096, "focus-victim": -0.8967152237892151, "focus-object": -1.144219994544983, "focus-concept": -0.4852965772151947}, "Woman killed by knife in Rome in front of 5 year-old daughter, husband arrested near Termini": {"blame-assassin": 1.210086703300476, "blame-victim": -0.4205947816371918, "blame-object": -0.07141570001840591, "blame-concept": -0.6498039364814758, "blame-none": -0.8642691373825073, "cause-human": 0.9806528687477112, "cause-object": 1.6627763509750366, "cause-concept": -0.09134841710329056, "cause-none": -0.21236413717269897, "focus-assassin": 1.301456332206726, "focus-victim": 0.45072898268699646, "focus-object": -0.2399590164422989, "focus-concept": -0.7384433746337891}, "Police suspect her husband": {"blame-assassin": 0.7433895468711853, "blame-victim": -0.01116070058196783, "blame-object": -0.590674638748169, "blame-concept": 0.18765106797218323, "blame-none": -0.71401047706604, "cause-human": 0.859955906867981, "cause-object": -0.8314203023910522, "cause-concept": -0.27798670530319214, "cause-none": -0.8475050330162048, "focus-assassin": 1.132333755493164, "focus-victim": -0.3831474184989929, "focus-object": -1.2223182916641235, "focus-concept": -0.6002827286720276}, "Deadly stabbing in Rome: Husband suspected of the woman's murder captured by police in the night.": {"blame-assassin": 1.0606005191802979, "blame-victim": -0.6035714149475098, "blame-object": -0.5695691704750061, "blame-concept": -0.747788667678833, "blame-none": -0.7839972376823425, "cause-human": 0.582966685295105, "cause-object": -0.2659671902656555, "cause-concept": -0.24072328209877014, "cause-none": -0.650257408618927, "focus-assassin": 1.2722291946411133, "focus-victim": -0.17269612848758698, "focus-object": -0.9622184634208679, "focus-concept": -0.7823270559310913}, "5-year-old daughter was at home at the time of the murder": {"blame-assassin": 0.31321707367897034, "blame-victim": 0.16416886448860168, "blame-object": -0.562633216381073, "blame-concept": -0.5603742003440857, "blame-none": 0.10873997211456299, "cause-human": 0.03977982699871063, "cause-object": -0.7193130850791931, "cause-concept": -0.678102970123291, "cause-none": 0.5020983815193176, "focus-assassin": -0.2061898559331894, "focus-victim": 0.8690225481987, "focus-object": -0.9299608469009399, "focus-concept": -0.36368128657341003}, "37-year-old Chinese woman killed by her husband in Quadraro in Rome": {"blame-assassin": 1.1681866645812988, "blame-victim": -0.10509136319160461, "blame-object": -0.36027368903160095, "blame-concept": -0.9057487845420837, "blame-none": -0.8369616270065308, "cause-human": 1.0208083391189575, "cause-object": -0.23398731648921967, 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"blame-concept": -0.9043233394622803, "blame-none": -0.7373567819595337, "cause-human": 0.7756348848342896, "cause-object": 0.9297934174537659, "cause-concept": -0.4901181757450104, "cause-none": -0.23380117118358612, "focus-assassin": 1.2856756448745728, "focus-victim": 0.41388165950775146, "focus-object": -0.5277608036994934, "focus-concept": -0.8828336596488953}, "Woman stabbed to death in her apartment in Rome.": {"blame-assassin": -0.17137572169303894, "blame-victim": -0.09856727719306946, "blame-object": -0.30918335914611816, "blame-concept": -0.8322544097900391, "blame-none": -0.3458186984062195, "cause-human": 0.43855559825897217, "cause-object": 0.6536886692047119, "cause-concept": -0.7885363698005676, "cause-none": 0.42713072896003723, "focus-assassin": -0.8842265009880066, "focus-victim": 0.5901778340339661, "focus-object": -0.2264055460691452, "focus-concept": -1.198877215385437}, "Woman stabbed in front of her daughter in her home: police suspects husband who may have fled": {"blame-assassin": 0.9140993356704712, "blame-victim": -0.37020084261894226, "blame-object": -0.25818777084350586, "blame-concept": -0.5793402194976807, "blame-none": -0.7495940327644348, "cause-human": 0.9052426815032959, "cause-object": 1.364701747894287, "cause-concept": -0.2882826626300812, "cause-none": -0.34797054529190063, "focus-assassin": 1.0512475967407227, "focus-victim": 0.20583266019821167, "focus-object": -0.5884801745414734, "focus-concept": -0.609580934047699}, "Neighbours report the couple often had violent arguments.": {"blame-assassin": 0.4963725507259369, "blame-victim": 0.4017191231250763, "blame-object": -0.537781298160553, "blame-concept": 0.5284959077835083, "blame-none": -0.6674476861953735, "cause-human": -0.17814767360687256, "cause-object": -0.8406278491020203, "cause-concept": 2.245201826095581, "cause-none": -0.7049922347068787, "focus-assassin": 0.31835004687309265, "focus-victim": -1.3500115871429443, "focus-object": -1.1312764883041382, "focus-concept": 0.24410690367221832}, "Femicide in Quadraro neighborhood: 37-year-old Chinese woman killed by her husband in Rome.": {"blame-assassin": 1.115739345550537, "blame-victim": -0.31499698758125305, "blame-object": -0.3950735330581665, "blame-concept": -0.6587293148040771, "blame-none": -0.7427958846092224, "cause-human": 0.8986117839813232, "cause-object": -0.2895506024360657, "cause-concept": -0.12052091956138611, "cause-none": 0.1557629257440567, "focus-assassin": 0.6100788712501526, "focus-victim": 0.7510258555412292, "focus-object": -1.0242074728012085, "focus-concept": -0.9053597450256348}, "Woman killed with kichen knife at home by her husband in front of 5 year-old daughter, Neighbors: \u201cI heard screams and noise and I went out but was late\u201d": {"blame-assassin": 1.0075547695159912, "blame-victim": -0.05372137948870659, "blame-object": 0.0658036395907402, "blame-concept": -0.30438971519470215, "blame-none": -0.7492923736572266, "cause-human": 0.9500102996826172, "cause-object": 1.7948311567306519, "cause-concept": 0.4919205904006958, "cause-none": -0.47972387075424194, "focus-assassin": 1.139479398727417, "focus-victim": 0.4496069848537445, "focus-object": 0.5468625426292419, "focus-concept": 0.0026139116380363703}, "The man has been arrested.": {"blame-assassin": -0.5218260288238525, "blame-victim": -0.0022120829671621323, "blame-object": -0.6286571621894836, "blame-concept": -0.9169755578041077, "blame-none": -0.3315635323524475, "cause-human": -0.6013137698173523, "cause-object": -0.641227662563324, "cause-concept": -0.6188435554504395, "cause-none": -0.106288842856884, "focus-assassin": 0.4209803640842438, "focus-victim": -1.2630500793457031, "focus-object": -1.0392670631408691, "focus-concept": -1.1582252979278564}, "\"Domestic Disturbance Turns Deadly: 37-Year-Old Chinese Woman Fatally Stabbed in Rome Apartment\"": {"blame-assassin": 0.5017176866531372, "blame-victim": -0.6858696937561035, "blame-object": -0.6850004196166992, "blame-concept": -0.7159314155578613, "blame-none": -0.8203385472297668, "cause-human": 0.5090516805648804, "cause-object": -0.7145772576332092, "cause-concept": -0.382533997297287, "cause-none": -0.9373898506164551, "focus-assassin": 0.5311318039894104, "focus-victim": 0.5167638063430786, "focus-object": -1.1940513849258423, "focus-concept": -0.5895946621894836}, "Rome, woman (37) killed by husband - It is the 72th woman murdered since the beginning of the year.": {"blame-assassin": 0.011273751966655254, "blame-victim": -0.11306155472993851, "blame-object": -0.39436474442481995, "blame-concept": 0.22645418345928192, "blame-none": -0.10378039628267288, "cause-human": 0.3545607924461365, "cause-object": -0.40012675523757935, "cause-concept": -0.3672570288181305, "cause-none": -0.05002939701080322, "focus-assassin": -0.48500895500183105, "focus-victim": 0.9500935673713684, "focus-object": -1.1109843254089355, "focus-concept": 0.16112205386161804}, "Man arrested for killing wife with a knife.": {"blame-assassin": 1.4547874927520752, "blame-victim": -0.2872142791748047, "blame-object": 0.07471797615289688, "blame-concept": -0.7417699098587036, "blame-none": -0.9875862002372742, "cause-human": 0.8910753726959229, "cause-object": 1.823077917098999, "cause-concept": 0.28888726234436035, "cause-none": -1.1123675107955933, "focus-assassin": 1.3377970457077026, "focus-victim": -0.43445882201194763, "focus-object": 0.8066223859786987, "focus-concept": -0.9867531061172485}, "Their daughter was in the house at the time of the murder": {"blame-assassin": 0.1601179987192154, "blame-victim": 0.020375730469822884, "blame-object": -0.5628160834312439, "blame-concept": -0.45164111256599426, "blame-none": 0.5345203876495361, "cause-human": 0.16643713414669037, "cause-object": -0.830133855342865, "cause-concept": -0.5411543250083923, "cause-none": 0.3129540979862213, "focus-assassin": 0.605758011341095, "focus-victim": 0.2011004090309143, "focus-object": -0.8596230745315552, "focus-concept": -0.5403037667274475}, "Woman stabbed to death at home in Rome.": {"blame-assassin": -0.28496143221855164, "blame-victim": -0.2007453441619873, "blame-object": -0.4163666367530823, "blame-concept": -0.7028643488883972, "blame-none": -0.37580615282058716, "cause-human": 0.3553897738456726, "cause-object": 0.5769342184066772, "cause-concept": -0.7591997385025024, "cause-none": 0.09372154623270035, "focus-assassin": -0.7395526766777039, "focus-victim": 0.6880345344543457, "focus-object": -0.665503740310669, "focus-concept": -1.0037411451339722}, " Rome, the man suspected to have killed by stabbyng his wife has been arrested.": {"blame-assassin": 1.4207465648651123, "blame-victim": -0.7538151144981384, "blame-object": -0.5759817957878113, "blame-concept": -0.7528424263000488, "blame-none": -1.1613351106643677, "cause-human": 0.7919354438781738, "cause-object": 0.9452908039093018, "cause-concept": 0.7420710921287537, "cause-none": -0.887161135673523, "focus-assassin": 1.4464675188064575, "focus-victim": -0.7464419007301331, "focus-object": -0.8630331158638, "focus-concept": -1.0808298587799072}, "The police tracked him down in the city, not far from Termini station": {"blame-assassin": -0.7669796347618103, "blame-victim": -0.7456995844841003, "blame-object": -0.6762293577194214, "blame-concept": -0.7183098196983337, "blame-none": 0.14837713539600372, "cause-human": -1.0511802434921265, "cause-object": -0.7607147097587585, "cause-concept": -0.7777164578437805, "cause-none": 0.6737768054008484, "focus-assassin": -0.6399377584457397, "focus-victim": -1.692221999168396, "focus-object": -0.5611519813537598, "focus-concept": -1.3268382549285889}, "Husband suspected of the murder, captured by police.": {"blame-assassin": 0.5298698544502258, "blame-victim": -0.5045542120933533, "blame-object": -0.6283653378486633, "blame-concept": -1.0429762601852417, "blame-none": -0.6862391233444214, "cause-human": 0.44710832834243774, "cause-object": -0.5471621155738831, "cause-concept": -0.4079943001270294, "cause-none": -0.5421837568283081, "focus-assassin": 1.3259085416793823, "focus-victim": -0.9495715498924255, "focus-object": -1.1535791158676147, "focus-concept": -1.1601359844207764}, "He was trying to run: Police found him in Termini.": {"blame-assassin": -0.30198195576667786, "blame-victim": -0.4484192728996277, "blame-object": -0.5976856350898743, "blame-concept": -0.7389621138572693, "blame-none": -0.0685170441865921, "cause-human": -0.3498765826225281, "cause-object": -0.554252564907074, "cause-concept": -0.2848188877105713, "cause-none": 0.24764908850193024, "focus-assassin": -0.18795429170131683, "focus-victim": -1.0568809509277344, "focus-object": -0.4850521385669708, "focus-concept": -1.0912580490112305}, "Police suspect this is the 22nd femicide in 2024": {"blame-assassin": -0.19046738743782043, "blame-victim": -0.6513540744781494, "blame-object": -0.6664432287216187, "blame-concept": -0.18935656547546387, "blame-none": 0.6988108158111572, "cause-human": -1.4068764448165894, "cause-object": -0.6692467927932739, "cause-concept": -0.7542968988418579, "cause-none": 0.15705320239067078, "focus-assassin": -0.9518113732337952, "focus-victim": -1.0043327808380127, "focus-object": -0.8320531249046326, "focus-concept": -0.6143154501914978}, "Woman stabbed to death in her apartment in Rome: husband fled, but was arrested by the police as main suspect.": {"blame-assassin": 0.9872367978096008, "blame-victim": -0.5761092305183411, "blame-object": -0.5285102725028992, "blame-concept": -0.3746843636035919, "blame-none": -0.8491857647895813, "cause-human": 0.9427244663238525, "cause-object": 0.529333770275116, "cause-concept": -0.32251065969467163, "cause-none": -0.4287147521972656, "focus-assassin": 1.2491176128387451, "focus-victim": 0.33297237753868103, "focus-object": -0.8794705271720886, "focus-concept": -0.8418151140213013}, "5-year-old was on the scene": {"blame-assassin": -0.813148021697998, "blame-victim": -0.6269714832305908, "blame-object": -0.7274290323257446, "blame-concept": -1.105955958366394, "blame-none": 0.9442123174667358, "cause-human": -1.083264946937561, "cause-object": -0.6712073683738708, "cause-concept": -0.7760385274887085, "cause-none": 0.04488767683506012, "focus-assassin": -0.7018630504608154, "focus-victim": -0.9367055296897888, "focus-object": -1.0543866157531738, "focus-concept": -1.0233051776885986}, "Most recent femicide in a series of cruel murders: husband suspected of stabbing his wife": {"blame-assassin": 1.1887542009353638, "blame-victim": -0.2560048997402191, "blame-object": -0.42848825454711914, "blame-concept": 0.44952723383903503, "blame-none": -0.9653131365776062, "cause-human": 0.8780454397201538, "cause-object": -0.16029557585716248, "cause-concept": 0.9233302474021912, "cause-none": -0.6964871883392334, "focus-assassin": 1.1396361589431763, "focus-victim": -0.988328754901886, "focus-object": -0.9110500812530518, "focus-concept": 0.010130726732313633}, "Husband on the run after allegedly stabbing his wife (37) to death during violent during a violent encounter in presence of her daughter (aged 5).": {"blame-assassin": 1.3119944334030151, "blame-victim": -0.12486863881349564, "blame-object": -0.20140978693962097, "blame-concept": 0.5548765063285828, "blame-none": -1.003324031829834, "cause-human": 1.1124749183654785, "cause-object": 1.6724741458892822, "cause-concept": 1.82231867313385, "cause-none": -0.840076208114624, "focus-assassin": 1.3908740282058716, "focus-victim": -0.2963496744632721, "focus-object": -0.5223827362060547, "focus-concept": -0.2490832805633545}, "Police arrests the presumed stabber of the Chinese woman": {"blame-assassin": 0.6856967210769653, "blame-victim": -0.46265366673469543, "blame-object": -0.6912520527839661, "blame-concept": -0.9345846176147461, "blame-none": -0.7630172371864319, "cause-human": 0.5118412375450134, "cause-object": 0.5221049785614014, "cause-concept": -0.5387157201766968, "cause-none": -1.0139508247375488, "focus-assassin": 1.2949694395065308, "focus-victim": -0.07526131719350815, "focus-object": -1.1318672895431519, "focus-concept": -1.1803098917007446}, "A woman was stabbed to death in Rome, allegedly by her husband, who was arrested overnight.": {"blame-assassin": 1.2826963663101196, "blame-victim": -0.22086584568023682, "blame-object": -0.5315461158752441, "blame-concept": -0.08872859179973602, "blame-none": -1.0393104553222656, "cause-human": 0.7930456399917603, "cause-object": 0.7769235372543335, "cause-concept": 0.15801392495632172, "cause-none": -0.9012068510055542, "focus-assassin": 1.347446084022522, "focus-victim": 0.5657009482383728, "focus-object": -0.6690416932106018, "focus-concept": -0.9772631525993347}}

app.py

@@ -0,0 +1,205 @@
+import streamlit as st
+import pandas as pd
+import json
+import nltk
+
+import re 
+
+nltk.download("punkt")
+
+with open("_perception_cache.json") as f:
+    s2s = json.load(f)
+with open("_analysis_cache.json") as f:
+    s2f = json.load(f)
+
+db = pd.read_excel("data.xlsx")
+
+FRAMES_OF_INTEREST = ["Abusing", "Attack", "Hit_target", "Quarreling", "Use_firearm", "Death", "Dead_or_alive", "Experience_bodily_harm", "Cause_harm", "Killing", "Event", "Catastrophe", "Offenses"]
+
+
+def get_frame_analysis(s):
+    frame_analysis = []
+    if s not in s2f:
+        return None
+    for fns in s2f[s]["sociofillmore"][0]["fn_structures"]:
+        # if True:
+        if fns["frame"] in FRAMES_OF_INTEREST:
+            analysis = {
+                "frame": fns["frame"],
+                "trigger": " ".join(fns["target"]["tokens_str"])
+            }
+            analysis.update({
+                rol[0]: " ".join(rol[1]["tokens_str"]) for rol in fns["roles"]
+            })
+            frame_analysis.append(analysis)
+
+    if len(frame_analysis) > 0:
+        return pd.DataFrame(frame_analysis)
+    else:
+        return None
+
+def analyze_document(doc):
+    if not pd.isna(doc):
+        sentences = nltk.sent_tokenize(
+            doc,
+            language="english"
+        )
+    else:
+        sentences = []
+
+    perception_tables = []
+    frame_tables = []
+    for si, s in enumerate(sentences[:20]):
+        frame_analysis_df = get_frame_analysis(s)
+        frame_tables.append(frame_analysis_df)
+        perception_tables.append(s2s.get(s))
+    return sentences, perception_tables, frame_tables
+
+
+st.write("# LorentzFillmore: WATCH YOUR LANGUAGE")
+
+st.dataframe(db)
+
+st.write("## Writing Exercises & Perception Scores")
+
+text_columns = [col for col in db.columns if col.startswith("Writing exercise:")]
+selected_column = st.selectbox(label="Writing exercise:", options=text_columns)
+aggregate_sentences = st.checkbox(label="Aggregate over sentences?")
+
+perception_rows = []
+for _, row in db.iterrows():
+    sentences, perception_tables, frame_tables = analyze_document(row[selected_column])
+    # mean_blame_score = pd.DataFrame(perception_tables).mean()["blame-assassin"]
+    
+    if aggregate_sentences:
+        perception_row = {
+            "writer": row["Email Address"], 
+            "gender": row["I identify as ..."], 
+            "language": row["What is your native language?"], 
+            "background": row["What is your background?"], 
+            "text": sentences
+        }
+        for k, v in pd.DataFrame(perception_tables).mean().to_dict().items():
+            perception_row[k] = v
+        perception_rows.append(perception_row)
+    else:
+        for s, pt, ft in zip(sentences, perception_tables, frame_tables):
+            perception_row = {
+                "writer": row["Email Address"], 
+                "gender": row["I identify as ..."], 
+                "language": row["What is your native language?"], 
+                "background": row["What is your background?"], 
+                "text": s
+            }
+            for k, v in pd.Series(pt).to_dict().items():
+                perception_row[k] = v
+            perception_rows.append(perception_row)
+
+perception_df = pd.DataFrame(perception_rows)
+
+dimension = st.selectbox(label="Which dimension of perception?", options=["blame", "cause", "focus"])
+dim_cols = [col for col in perception_df.columns if col.startswith(dimension)]
+dim_df = (
+    perception_df[["writer", "text"] + dim_cols]
+    .style.background_gradient(subset=dim_cols, axis=None, vmin=-2, vmax=2, cmap="YlGnBu")
+
+)
+st.dataframe(dim_df)
+
+st.write("### Analysis by demographic attribute")
+
+demo_attrib = st.selectbox("Select demographic attribute:", options=["writer", "gender", "language", "background"])
+perc_attrib = st.selectbox("Select perception attribute", options=dim_cols)
+st.plotly_chart(perception_df.groupby(demo_attrib).agg({perc_attrib: "mean"}).plot.bar(backend="plotly"))
+
+
+st.write("## Comparing versions")
+v_number = int(re.search(r"version (\d)", selected_column).group(1))
+if v_number < 2:
+    st.warning("To compare versions, select a writing exercise with version number 2 or higher.")
+else:
+    prev_version = re.sub(r"version (\d)", f"version {v_number - 1}", selected_column)
+    assert prev_version in text_columns
+    st.info(f"Comparing _{selected_column.replace('Writing exercise: ', '')}_ ↔️ _{prev_version.replace('Writing exercise: ', '')}_")
+
+    perception_diff_rows = []
+    for _, row in db.iterrows():
+        sentences, perception_tables, frame_tables = analyze_document(row[selected_column])
+        prev_sentences, prev_perception_tables, prev_frame_tables = analyze_document(row[prev_version])
+        
+        perception_diff_row = {
+            "writer": row["Email Address"], 
+            "gender": row["I identify as ..."], 
+            "language": row["What is your native language?"], 
+            "background": row["What is your background?"], 
+            f"text_v{v_number - 1}": prev_sentences,
+            f"text_v{v_number}": sentences
+        }
+        perc_new = pd.DataFrame(perception_tables).mean().to_dict()
+        perc_old = pd.DataFrame(prev_perception_tables).mean().to_dict()
+        for k, v in perc_new.items():
+            if k not in perc_old:
+                perception_diff_row[k] = 0    
+            else:
+                perception_diff_row[k] = v - perc_old[k]
+        perception_diff_rows.append(perception_diff_row)
+
+    perception_diff_df = pd.DataFrame(perception_diff_rows)
+
+    dim_diff_df = (
+        perception_diff_df[["writer", f"text_v{v_number - 1}", f"text_v{v_number}"] + dim_cols]
+        .style.background_gradient(subset=dim_cols, axis=None, vmin=-2, vmax=2, cmap="YlGnBu")
+
+    )
+    st.dataframe(dim_diff_df)
+
+    st.write("### Analysis by demographic attribute")
+
+    demo_attrib_diff = st.selectbox("Select demographic attribute for diff:", options=["writer", "gender", "language", "background"])
+    perc_attrib_diff = st.selectbox("Select perception attribute for diff", options=dim_cols)
+    st.plotly_chart(perception_diff_df.groupby(demo_attrib_diff).agg({perc_attrib_diff: "mean"}).plot.bar(backend="plotly"))
+
+
+st.write("## Frame analysis")
+only_sentences_with_relevant_frames = st.checkbox("Only analyze sentences containing relevant frames?")
+
+selected_writer = st.selectbox(
+    label="Select a writer:",
+    options=sorted([f"{row['Email Address']}" for idx, row in db.iterrows()])
+)
+
+st.write("----")
+
+writer_row = db[db["Email Address"] == selected_writer].iloc[0]
+
+st.write("### Text information")
+st.dataframe(writer_row)
+
+sentences, perception_tables, frame_tables = analyze_document(writer_row[selected_column])
+
+
+st.write("### Analysis of the entire document")
+mean_perception = pd.DataFrame(perception_tables).mean().to_frame(name="mean perception")
+st.dataframe(mean_perception.style.highlight_max(axis=0), width=500)
+
+st.write("---")
+st.write("### Analysis by sentence")
+
+for si, s in enumerate(sentences[:20]):
+    frame_analysis_df = frame_tables[si]
+    if frame_analysis_df is None and only_sentences_with_relevant_frames:
+        continue
+
+    st.write(f"#### Sentence #{1+si:02}/{len(sentences[:20])}\n*{s}*")
+    if s not in s2s or s not in s2f:
+        st.write("(Analysis not found)")
+        continue
+
+    st.write("##### Perception")
+    perception_table = perception_tables[si]
+    perception_df = pd.Series(perception_table).to_frame(name="predicted perception").style.highlight_max(axis=0)
+    st.dataframe(perception_df, width=500)
+
+    if frame_analysis_df is not None:
+        st.write("##### Relevant frames")
+        st.dataframe(frame_analysis_df, width=750)

requirements.txt

@@ -0,0 +1,113 @@
+altair==5.3.0
+asttokens==2.4.1
+attrs==23.2.0
+backcall==0.2.0
+blinker==1.7.0
+cachetools==5.3.3
+captum==0.7.0
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+comm==0.2.2
+contourpy==1.2.1
+cycler==0.12.1
+debugpy==1.8.1
+decorator==5.1.1
+et-xmlfile==1.1.0
+exceptiongroup==1.2.0
+executing==2.0.1
+filelock==3.13.3
+fonttools==4.51.0
+fsspec==2024.3.1
+gitdb==4.0.11
+GitPython==3.1.43
+huggingface-hub==0.22.2
+idna==3.6
+importlib_metadata==7.1.0
+importlib_resources==6.4.0
+ipykernel==6.29.4
+ipython==7.34.0
+jedi==0.19.1
+Jinja2==3.1.3
+joblib==1.4.0
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+jupyter_client==8.6.1
+jupyter_core==5.7.2
+kiwisolver==1.4.5
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.4
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+mpmath==1.3.0
+nest-asyncio==1.6.0
+networkx==3.2.1
+nltk==3.8.1
+numpy==1.26.4
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.19.3
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.1.105
+openpyxl==3.1.2
+packaging==24.0
+pandas==2.2.1
+parso==0.8.4
+pexpect==4.9.0
+pickleshare==0.7.5
+pillow==10.3.0
+platformdirs==4.2.0
+plotly==5.20.0
+prompt-toolkit==3.0.43
+protobuf==4.25.3
+psutil==5.9.8
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==15.0.2
+pydeck==0.8.1b0
+Pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+PyYAML==6.0.1
+pyzmq==25.1.2
+referencing==0.34.0
+regex==2023.12.25
+requests==2.31.0
+rich==13.7.1
+rpds-py==0.18.0
+sacremoses==0.1.1
+safetensors==0.4.2
+scikit-learn==1.0.2
+scipy==1.13.0
+six==1.16.0
+smmap==5.0.1
+stack-data==0.6.3
+streamlit==1.33.0
+sympy==1.12
+tenacity==8.2.3
+threadpoolctl==3.4.0
+tokenizers==0.10.3
+toml==0.10.2
+toolz==0.12.1
+torch==2.2.2
+tornado==6.4
+tqdm==4.66.2
+traitlets==5.14.2
+transformers==4.15.0
+transformers-interpret==0.10.0
+triton==2.2.0
+typing_extensions==4.11.0
+tzdata==2024.1
+urllib3==2.2.1
+watchdog==4.0.0
+wcwidth==0.2.13
+zipp==3.18.1

z_scorer.pickle

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9d86c3866d76f5074982f901aa3fd93abbff7225d6c57cae92f737bd4d5cb02
+size 761